Your agents need knowledge,
not another database

What is a knowledge graph?

Nodes are facts — concepts, operators, frameworks, cases. Edges are relationships — teaches, extends, illustrates. Confidence encodes trust. Click any node to see how traversal discovers connected knowledge.

Node color = type · brightness = confidence · click to traverse

Not a graph database. A knowledge engine.

Graph databases store and query relationships. h2t-graphs goes further — it scores, ranks, tracks provenance, and serves LLM-optimized subgraphs. Your agent gets answers, not rows.

Engine owns infrastructure. Domain owns knowledge.

The engine knows nothing about Houdini, TouchDesigner, or creative thinking. It stores, scores, queries, and validates. Consumers bring domain-specific data and get domain-specific answers.

graph LR
    subgraph DOMAIN ["YOUR DOMAIN"]
        D1["Schemas"] & D2["Fields"] & D3["Review"]
    end
    subgraph ENGINE ["h2t-graphs"]
        E1["Store"] & E2["Score"] & E3["Search"] & E4["Validate"]
    end
    subgraph AGENTS ["AI AGENTS"]
        A1["Query"] & A2["Write"] & A3["Feedback"]
    end
    DOMAIN -->|"data"| ENGINE
    ENGINE -->|"answers"| AGENTS
    AGENTS -->|"feedback"| ENGINE
    style ENGINE fill:#1a0a10,stroke:#e94560,color:#e94560
    style DOMAIN fill:#0a0d1a,stroke:#4a9eff,color:#c0c0d0
    style AGENTS fill:#0a1a0d,stroke:#00ff88,color:#c0c0d0

Currently serving: creative methodologies, DCC operators, educational courses, TRIZ principles, design thinking frameworks

Four search modes. One API call.

Keyword precision, semantic understanding, or both. Query expansion enriches searches with LLM-generated synonyms. Edge-enriched embeddings capture graph context, not just node text.

Every node has a trust score

Not all knowledge is equal. LLM-generated content starts at low confidence. Expert-verified content earns trust. The engine tracks the journey.

graph LR
    M["MODEL
confidence: 0.3"] -->|"human review"| S["SESSION
confidence: 0.6"]
    S -->|"expert validation"| E["EXPERT
confidence: 0.9"]
    E -->|"feedback refines"| S
    style M fill:#0e0e14,stroke:#3a3a50,color:#a0a0b8
    style S fill:#0a0d1a,stroke:#4a9eff,color:#4a9eff
    style E fill:#0a1a0d,stroke:#00ff88,color:#00ff88,stroke-width:2px

Source types: expert (0.8–1.0) · docs (0.9) · session (0.6–0.8) · model (0.3–0.5). Queries filter by min_confidence. Feedback adjusts scores in real time.

Write a node. Search it instantly.

On-write embedding — every new node is automatically vectorized with edge context. No batch jobs, no waiting. The node is semantically searchable the moment it's written.

# Write a node via API
POST /api/nodes
{
  "source": "my-domain",
  "node": {"id": "insight_42", "node_type": "insight", "label": "Pattern discovered", ...}
}

→ {"status": "ok", "node_id": "insight_42", "embedded": true}

# Immediately searchable
GET /api/query?source=my-domain&semantic=pattern+discovery
→ results include insight_42

Part of a self-improving ecosystem

h2t-graphs is the persistence layer in a larger feedback loop. Evaluation, optimization, and continuous learning flow through the graph.

graph LR
    subgraph CLIENTS ["CLIENTS"]
        C1["DCC Skill"] & C2["Creative"] & C3["Bot"]
    end
    subgraph GRAPHS ["h2t-graphs"]
        G1["Store"] & G2["Search"] & G3["Score"]
    end
    subgraph EVALS ["h2t-evals"]
        V1["GT Packs"] & V2["Judge"] & V3["Optimizer"]
    end
    CLIENTS -->|"query"| GRAPHS
    GRAPHS -->|"metrics"| EVALS
    EVALS -->|"improve"| CLIENTS
    style GRAPHS fill:#1a0a10,stroke:#e94560,color:#e94560
    style CLIENTS fill:#0a1a0d,stroke:#00ff88,color:#c0c0d0
    style EVALS fill:#0a0d1a,stroke:#4a9eff,color:#c0c0d0

Built for solo + AI teams

Lightweight. No Kubernetes. No managed services. One VPS, auto-TLS, full control.